Digital Twin and Ml-basEd MOdel of TEVAR Interventions (MEMO)

The study aims to collect clinical data and pseudonymized CT images of patients undergoing TEVAR in order to create an anatomical digital twin capable of simulating procedural outcomes and training machine learning (ML) algorithms. This approach will support predictive models that may assist physicians in selecting the optimal medical device, improving pre-TEVAR planning, and predicting post-TEVAR complications.

Study Overview

• Status: Recruiting
• Conditions: Aorta Disease; Aorta, Thoracic Pathologies

Detailed Description

In recent years, Thoracic Endovascular Aortic Repair (TEVAR) has become increasingly utilized for the treatment of thoracic aortic pathologies. Over the past two decades, the adoption of TEVAR has grown significantly, progressively replacing open surgery as the preferred treatment approach in many cases. Initially designed for interventions involving the descending thoracic aorta, TEVAR is now being extended to more complex anatomies, including the aortic arch and even regions closer to the aortic root.

Successful TEVAR procedures rely on accurate preoperative planning and detailed clinical assessment to optimize patient outcomes. Although TEVAR offers several advantages over open surgery, including reduced procedural risk, shorter recovery time, and lower morbidity, it is not without limitations. Major complications include endoleaks, stent-induced new entry tears, vessel obstruction, and stent migration, all of which may significantly affect patient prognosis. Despite existing manufacturer guidelines and deployment strategies, these complications remain difficult to predict.

Previous studies have reported endoleak rates ranging from 4% to 15%, stent migration rates between 1.0% and 2.8%, and device-related complications occurring in up to 38% of cases. Recent advances in computational modeling have demonstrated considerable potential for improving TEVAR planning and risk prediction. Finite element analysis (FEA) and fluid-structure interaction (FSI) simulations have proven valuable for assessing stent behavior within patient-specific anatomies. Through in silico simulations, different stent types and diameter configurations can be virtually tested, providing surgeons with critical insights for clinical decision-making.

However, despite their high accuracy, these techniques are computationally intensive and require large datasets as well as specialized expertise, limiting their accessibility for routine clinical practice. To address these challenges, numerical models (e.g., finite element simulations) and machine learning (ML) approaches represent promising alternatives for real-time, data-driven perioperative decision support. By integrating finite element simulations with clinical imaging data, ML algorithms can be trained to predict procedural outcomes, optimize prosthesis selection, and estimate post-interventional risks. This approach not only enhances pre-procedural planning but also facilitates postoperative risk assessment, ultimately contributing to improved patient management.

A critical challenge in developing robust ML models for TEVAR planning is the limited accessibility of high-quality annotated datasets and their integration into clinical workflows. To overcome this limitation, the study proposes a comprehensive methodology aimed at:

I) collecting clinical and imaging data relevant to TEVAR procedures; II) augmenting patient-specific anatomical data using statistical shape modeling (SSM) to generate a diverse training dataset; III) developing high-fidelity digital twins that provide personalized virtual replicas of individual TEVAR cases; and IV) training ML models on these augmented datasets to predict procedural outcomes based on patient-specific characteristics.

Using these techniques, the study aims to develop a clinically viable framework capable of predicting surgical outcomes and increasing the information available for surgeons during preoperative decision-making, thereby improving patient outcomes in TEVAR interventions.

• Study Type: Observational
• Enrollment (Estimated): 5000

Contacts and Locations

• Study Contact: Name: SANTI TRIMARCHI, MD, PHD; Phone Number: +390255032438; Email: santi.trimarchi@policlinico.mi.it

Study Locations

• Italy
  • Milan, Italy
    • Recruiting
    • Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico
    • Contact: SANTI TRIMARCHI, MD, PHD; Phone Number: +390255032438; Email: santi.trimarchi@policlinico.mi.it

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

patients undergoing TEVAR

Description

Inclusion Criteria:

• ≥18 Years and older (Adult, Older Adult)
• Female and male
• Received TEVAR for: Chronic or acute dissection, Aneurysm, Penetrating aortic ulcer, aortic thrombus, intramural hematoma or traumatic injury

Exclusion Criteria:

• Younger than 18 years old
• Received TEVAR in surgical graft that replaced native aorta
• Poor CT image quality that leads to failure in generating a high-fidelity 3D FE model of patient anatomy (no preoperative multidetector contrast-enhanced CT-scan available, preoperative CTscan slice thickness greater than 1mm, preoperative CT-scan with artifacts, motion artifacts due to the presence of other implanted devices affecting the region of interest)

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Determine the accuracy of patient-specific numerical simulations in replicating TEVAR deployment outcomes	Accuracy of the simulations, expressed in terms of the match between simulated and post-operative device-vessel interaction (e.g., configuration, sealing quality, apposition), as assessed via comparison of post-operative CT image with the simulation results	up to 1 year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Assess the predictive performance of the ML model in forecasting clinical complications	Sensitivity, specificity, and AUC of the model in predicting complications using retrospective clinical follow-up data	up to 1 year

Collaborators and Investigators

• Sponsor: Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico

Study record dates

Study Major Dates

• Study Start (Actual): February 11, 2026
• Primary Completion (Estimated): September 30, 2026
• Study Completion (Estimated): September 30, 2026

Study Registration Dates

• First Submitted: May 4, 2026
• First Submitted That Met QC Criteria: June 5, 2026
• First Posted (Actual): June 11, 2026

Study Record Updates

• Last Update Posted (Actual): June 11, 2026
• Last Update Submitted That Met QC Criteria: June 5, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: machine learning; thoracic aorta; Finite Element Analysis
• Additional Relevant MeSH Terms: Vascular Diseases; Cardiovascular Diseases; Aortic Diseases
• Other Study ID Numbers: 6492

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No